Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments Training Course

Foundations of Sovereign AI

• What sovereign AI means in regulated organizations
• Business, legal, and operational drivers
• Core control areas: data, models, infrastructure, and operations

Regulatory Requirements and Risk Mapping

• Data residency, privacy, and sector-specific obligations
• Mapping sensitive data to AI use cases
• Identifying cross-border, logging, and third-party exposure risks

Governing Data, Prompts, and Logs

• Prompt governance and acceptable use boundaries
• Logging policies for prompts, responses, and metadata
• Retention, redaction, masking, and access control practices
• Exercise: reviewing an AI data flow for governance gaps

Model Hosting and Inference Environment Options

• Public API, private cloud, on-premise, and hybrid deployment choices
• Factors for deciding where models should run
• Trade-offs among control, security, cost, and operational ownership

Vendor Dependence and Portability

• Common lock-in patterns in models, tools, and platforms
• Portability through modular architecture, open interfaces, and clear contracts
• Exercise: evaluating a vendor against sovereignty criteria

Governance Model and Action Planning

• Roles and responsibilities across IT, security, legal, and compliance
• Approval workflows for use cases, models, and operational changes
• Auditability, monitoring, and incident response expectations
• Building a practical sovereign AI roadmap and next steps